A three-dimensional display method and system for PBAT degradation film materials

Abstract

The present invention discloses a 3D display method and system of PBAT degradation film materials, the method includes obtaining electron microscope projection images of PBAT degradation film material specimens in different directions, segmenting each electron microscope projection image to obtain each initial sub-image, forming group domain image data, tracking and curve fitting the selected fiber initial contour points in each sub-image to obtain a first contour line segment set, obtaining the first fiber size data and the second fiber size data corresponding to each sub-image, screening the first fiber size data according to a clustering algorithm to obtain target fiber size data, and reconstructing the three-dimensional direction of each group domain image data after Fourier transformation based on the target fiber size data to obtain the three-dimensional display information corresponding to the PBAT degradation film material specimen. A 3D display method and system of PBAT degradation film materials provided by an embodiment of the present invention improves the comprehensiveness of the three-dimensional display of PBAT degradation film materials.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a three-dimensional display method and system for PBAT degradation film materials. Background Art

[0002] PBAT biodegradable film material belongs to thermoplastic biodegradable plastics, which is a copolymer of butylene adipate and butylene terephthalate. It has the characteristics of both PBA and PBT, and has good ductility and elongation at break, as well as good heat resistance, impact resistance and biodegradability. With the improvement of people's environmental awareness and the increase in demand for biodegradable plastics, PBAT biodegradable film material, as an excellent biodegradable plastic, has broad application prospects and huge market potential.

[0003] In recent years, existing technologies have studied the macroscopic properties of PBAT degradation membrane materials, such as the mechanical properties and degradation properties of materials. From a microscopic perspective, it is the fiber properties within the material that determine the macroscopic properties of PBAT degradation membrane materials. In order to further fully display the microscopic details of PBAT degradation membrane materials, a method that can accurately display the internal details of PBAT degradation membrane materials in three dimensions is urgently needed. Summary of the invention

[0004] The present invention provides a three-dimensional display method and system for PBAT degradation film materials. By performing a series of processing on a two-dimensional electron microscope projection image of the PBAT degradation film material, an accurate three-dimensional display result of the PBAT degradation film material can be obtained, thereby advancing the research progress on the PBAT degradation film material.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a three-dimensional display method of PBAT degradation film material, comprising:

[0006] Obtain electron microscope projection images of PBAT degradation membrane material specimens in different directions;

[0007] Segmenting each of the electron microscope projection images based on the maximum inter-class variance method, and binarizing each of the obtained initial sub-images consisting of at most one nonlinear segment fiber to obtain group domain image data consisting of each of the binarized sub-images;

[0008] Tracking the initial contour points of the fibers selected in each of the sub-images, and performing curve fitting on the coordinates of the contour points obtained by tracking based on the neural network technology to obtain a first contour line segment set in each of the sub-images;

[0009] Using an edge algorithm to calculate the first contour line segment set to obtain first fiber size data corresponding to each of the sub-images;

[0010] By scanning the pixel point information of each set of the first contour line segments, each second fiber size data is obtained, and the first fiber size data is screened according to a clustering algorithm to obtain target fiber size data, wherein the screening process is designed to use the corresponding second fiber size data as a distance metric for clustering;

[0011] Based on the Fourier projection slice theorem and the target fiber size data, the image data of each group domain after Fourier transformation is reconstructed in three dimensions to obtain the three-dimensional display information corresponding to the PBAT degradation membrane material specimen.

[0012] As one of the preferred solutions, the segmentation process of each electron microscope projection image based on the maximum inter-class variance method includes:

[0013] Performing grayscale processing on each of the electron microscope projection images, and calculating the occurrence probability of each grayscale level in the processing result;

[0014] According to the occurrence probabilities, respectively calculate the pixel probabilities of the fiber crossing nodes in each electron microscope projection image, the pixel probabilities of the fiber bonding nodes in each electron microscope projection image, the pixel probabilities of the linear fiber nodes in each electron microscope projection image, and the pixel probabilities of the background images in each electron microscope projection image; and,

[0015] Calculate the average gray value of the fiber intersection nodes in each electron microscope projection image, the average gray value of the fiber bonding nodes in each electron microscope projection image, the average gray value of the linear fiber nodes in each electron microscope projection image, and the average gray value of the background image in each electron microscope projection image;

[0016] Calculate the corresponding inter-class variance according to the pixel probability and the average gray value;

[0017] The calculation results of the inter-class variance are traversed to determine the segmentation result corresponding to each of the electron microscope projection images.

[0018] As one preferred solution, before binarizing each of the obtained initial sub-images consisting of at most one nonlinear fiber segment, the method further comprises:

[0019] Performing histogram equalization processing on the grayscale histogram of each of the obtained initial sub-images;

[0020] Based on the equalization processing result, determining the grayscale boundary threshold corresponding to each of the initial sub-images, wherein the grayscale boundary threshold at least includes a grayscale maximum value and a grayscale minimum value;

[0021] Each pixel point in each of the initial sub-images is processed by a linear stretching formula, and a critical threshold corresponding to the binarization process is determined by the Otsu algorithm.

[0022] As one of the preferred solutions, the initial contour points of the fibers selected in each of the sub-images are tracked, and the coordinates of the contour points obtained by tracking are curve-fitted based on the neural network technology to obtain a first contour line segment set in each of the sub-images, including:

[0023] Performing dilation and erosion operations on each of the sub-images in turn;

[0024] Based on an edge detection algorithm, determining initial fiber contour points in each of the processed sub-images;

[0025] Performing feature analysis on the initial fiber contour points, wherein the feature analysis process at least includes analyzing the gradient, curvature, normal and neighborhood features of the initial fiber contour points;

[0026] The points determined by the feature analysis are used as a set of contour point coordinates participating in the neural network training, and curve fitting is performed on the contour point coordinate set based on a first power function and / or a second power function to obtain a first contour line segment set in each of the sub-images.

[0027] As one of the preferred solutions, the expansion operation processing includes the following calculation formula:

[0028]

[0029] in, S1 ( x , y ) is the selected dilation structure element, and A is the area within the sub-image;

[0030] The corrosion operation process includes the following calculation formula:

[0031]

[0032] in, S2 ( x , y ) is the selected erosion structure element, and A is the area within the sub-image.

[0033] As a preferred solution, the second fiber size data at least includes fiber diameter data;

[0034] The step of obtaining each second fiber size data by scanning the pixel point information of each first contour line segment set includes:

[0035] Connecting each line segment node in each of the first contour line segment sets to determine a two-dimensional area region within each of the sub-images;

[0036] Scan the total number of pixels in each area region to obtain area data and side length data in each sub-image;

[0037] The area data and the side length data are processed to obtain fiber diameter data in each sub-image.

[0038] As one preferred solution, the first fiber size data is screened according to a clustering algorithm to obtain target fiber size data, including:

[0039] According to the pre-constructed noise point mapping relationship, the first fiber size data is subjected to primary screening to obtain corresponding primary result data;

[0040] According to the pre-constructed fiber intersection mapping relationship, the primary result data is subjected to secondary screening to obtain corresponding secondary result data;

[0041] According to the pre-constructed bonding point mapping relationship, the secondary result data is subjected to a tertiary screening to obtain corresponding tertiary result data;

[0042] Based on the second fiber size data, the cluster center of the K-Means clustering algorithm is determined to screen the tertiary result data.

[0043] As one of the preferred solutions, based on the Fourier projection slice theorem and the target fiber size data, the image data of each group domain after Fourier transformation is reconstructed in three dimensions to obtain the three-dimensional display information corresponding to the PBAT degradation membrane material specimen, including:

[0044] Determining phase shift information of the electron wave function corresponding to each of the groups of domain image data;

[0045] According to the signal amount scattered at a specific angle and the spatial spectrum of the PBAT degradation film material specimen, the phase shift information is reversely deduced to determine the scattering direction corresponding to each group of domain image data;

[0046] Image fitting is performed based on the scattering direction and the target fiber size data, a reconstructed three-dimensional structure is obtained from a Fourier domain filled with a two-dimensional Fourier transform of the fitting result by a three-dimensional inverse Fourier transform, and the three-dimensional structure is displayed in three dimensions.

[0047] As one preferred solution, the three-dimensional display of the three-dimensional structure includes:

[0048] Performing big data analysis on the three-dimensional structure to obtain fiber voxel information corresponding to the three-dimensional structure, wherein the voxel information includes at least color information, texture information, crack information, and hole information;

[0049] Based on the WebGL graphics technology standard, determining the point cloud rendering scale corresponding to the fiber voxel information;

[0050] The three-dimensional structure is rendered and displayed at the point cloud rendering scale.

[0051] Another embodiment of the present invention provides a three-dimensional display system for PBAT degradable film materials, the system comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the three-dimensional display method of PBAT degradable film materials as described above when executing the computer program.

[0052] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0053] (1) Segment the two-dimensional electron microscope projection image reflecting the microscopic details of the PBAT degradation film material to reduce the influence of noise and interference in the image, highlight the fiber outline in the material, and provide accurate data support for subsequent three-dimensional display;

[0054] (2) In order to obtain accurate material fiber information and improve the accuracy of the final three-dimensional display, this scheme classifies the fibers in the material in a targeted manner and incorporates linear segments and at most one nonlinear segment into the sub-image, thereby avoiding the boundary blurring caused by the crossing and bonding of too many nonlinear segment fibers in the image;

[0055] (3) By tracking the information of the initial fiber contour points, neural network technology is used to fit the curve. On the one hand, it can approximate a given multivariable continuous function with arbitrary precision, which enables the neural network to handle complex nonlinear relationships and accurately fit various fiber contour curves; on the other hand, the neural network has adaptive and autonomous learning capabilities and can automatically adjust its internal parameters through training to minimize prediction errors. This ability enables the neural network to self-optimize according to different data sets and problems, thereby improving the accuracy and robustness of the fitted fiber contour curve;

[0056] (4) Comprehensive analysis of the original two-dimensional images can improve the display resolution of the three-dimensional structure, making up for the lack of research on the microscopic level of PBAT degradation membrane materials in existing technologies, and helping to more intuitively understand the complex three-dimensional structure, shape and spatial relationship of PBAT degradation membrane materials, thereby promoting the intelligent research process of PBAT degradation membrane materials at a deeper level. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of a process of a three-dimensional display method of a PBAT degradation film material in one embodiment of the present invention;

[0058] Figure 2 It is a schematic diagram of the processing flow of each initial sub-image obtained by segmenting the electron microscope projection image in one embodiment of the present invention;

[0059] Figure 3 It is a structural block diagram of a three-dimensional display system of PBAT degradation film materials in one embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0062] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0063] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0064] One embodiment of the present invention provides a three-dimensional display method for PBAT degradation film materials. For details, see Figure 1 , Figure 1 The figure shows a schematic flow chart of a three-dimensional display method of a PBAT degradation film material in one embodiment of the present invention, which specifically includes steps S1 to S6:

[0065] S1. Obtain electron microscope projection images of PBAT degradation film material specimens in different directions;

[0066] S2. Segment each of the electron microscope projection images based on the maximum inter-class variance method, and perform binarization on each of the initial sub-images consisting of at most one nonlinear segment fiber to obtain group domain image data consisting of each binarized sub-image; for ease of understanding, Figure 2 FIG. 1 is a schematic diagram of a processing flow of each initial sub-image obtained by segmenting an electron microscope projection image, wherein 9 initial sub-images are shown, and each initial sub-image includes at most one nonlinear segment fiber. In an embodiment of the present invention, the nonlinear segment fiber includes but is not limited to fiber intersections and bonding points, and Figure 2 The initial sub-images are only used as examples to facilitate subsequent understanding and will not be described in detail here;

[0067] S3, tracking the initial contour points of the fibers selected in each of the sub-images, and performing curve fitting on the coordinates of the contour points obtained by tracking based on a neural network technique to obtain a first contour line segment set in each of the sub-images;

[0068] S4, using an edge algorithm to calculate the first contour line segment set to obtain first fiber size data corresponding to each of the sub-images;

[0069] S5, obtaining each second fiber size data by scanning the pixel point information of each first contour line segment set, and screening the first fiber size data according to a clustering algorithm to obtain target fiber size data, wherein the screening process is designed to use the corresponding second fiber size data as a distance metric for clustering;

[0070] S6. Based on the Fourier projection slice theorem and the target fiber size data, the image data of each group domain after Fourier transformation is reconstructed in three dimensions to obtain the three-dimensional display information corresponding to the PBAT degradation membrane material specimen.

[0071] It should be noted in advance that in the embodiment of the present invention, it is necessary to display the PBAT degradation film material specimen in three dimensions. The specimen can be sliced ​​and then a scanning electron microscope of a corresponding model is used to collect electron microscope projection images of the slices to provide processing data for subsequent image processing and analysis. Preferably, the scanning electron microscope is a Japanese JSM.7610F with a resolution of 1.0nm (15kV) / 1.3nm (1kV) and a magnification of 750,000 times. Of course, those skilled in the art can also choose other types of scanning electron microscopes, which are not specifically limited in the embodiment of the present invention.

[0072] It should be emphasized that the imaging principle of the electron microscope projection image is realized by utilizing the interaction between electrons and slices / specimens. Due to the embodiment of the present invention, the above-mentioned step S6 includes an optimization and improvement step of processing the two-dimensional electron microscope projection image to achieve three-dimensional display. For ease of understanding, the imaging process is also briefly explained here. For example, when electrons are emitted to slices / specimens with large mass and density, the main imaging effect is scattering. The coarse fibers with large mass thickness on the slices / specimens have a large scattering angle for electrons, and fewer electrons pass through; when the electron beam is diffracted by the slices / specimens, the amplitude distribution of the diffraction waves at different positions corresponds to the different diffraction abilities of the fiber parts in the slices / specimens; when the slices / specimens are thin to below a certain thickness, electrons can pass through the slices / specimens, and the amplitude change of the wave can be ignored. The imaging at this time comes from the change in phase, which will be explained in detail in conjunction with the principle later.

[0073] In the above embodiment, since a single electron microscope projection image contains a large amount of noise, it is difficult to accurately analyze a single material fiber. Therefore, the embodiment of the present invention performs segmentation processing on each of the electron microscope projection images based on the maximum inter-class variance method, which specifically includes the following steps:

[0074] Grayscale processing is performed on each of the electron microscope projection images, and the occurrence probability of each grayscale level in the processing result is calculated. The calculation is performed using the following formula:

[0075]

[0076] Among them, Pi is the probability of occurrence of each gray level, k is the number of pixels with gray level i, and m is the total number of pixels in the image.

[0077] In order to classify the fibers in a targeted manner and improve the image segmentation effect, this embodiment divides the fibers into fiber crossing nodes, fiber bonding nodes, linear fiber nodes and background images, and calculates the pixel probability and average gray value of the above parameters respectively to calculate the corresponding inter-class variance. The specific inter-class variance calculation formula is as follows:

[0078]

[0079] in, is the maximum variance of the fiber crossing node, fiber bonding node, linear fiber node and the background image, is the average gray value of fiber crossing nodes, fiber bonding nodes, and linear fiber nodes, is the average gray value of the background image, is the pixel probability of fiber crossing nodes, fiber bonding nodes, and linear fiber nodes, is the pixel probability of the background image.

[0080] The above formula measures the difference between the target image and the background image. When part of the target is mistakenly classified as the background, or part of the background is mistakenly classified as the target, the inter-class variance will become smaller. The threshold is traversed from 0 to 255. For each possible threshold, the inter-class variance is recalculated to find the threshold that maximizes the inter-class variance. This threshold is the optimal threshold. Through the above steps, the maximum inter-class variance method can automatically determine an optimal threshold to maximize the difference between the target part and the background part of the image, thereby realizing the binary segmentation of the image, that is, determining the segmentation result corresponding to each of the electron microscope projection images.

[0081] Of course, in order to improve the result of the binarization process, the embodiment of the present invention adopts linear stretching for processing, and linear stretching can improve the contrast of the image, making the grayscale difference between the target and the background in the image more obvious, which helps to more accurately segment the target and the background during the binarization process. In addition, although the linear stretching itself does not directly remove noise, it can reduce the influence of noise on the binarization process by improving the image contrast. Linear stretching before the binarization process can make the noise in the image less obvious, thereby improving the accuracy of the binarization process. The present embodiment adopts a linear stretching method to expand the overall grayscale value of the image to 0~255 pixels in contrast enhancement. The Otsu algorithm is used to find the threshold value of the image after grayscale enhancement to complete the binarization of the image. Specifically, firstly, a histogram equalization process is performed on the grayscale histogram of each of the initial sub-images obtained; then, based on the equalization process result, a grayscale boundary threshold corresponding to each of the initial sub-images is determined, wherein the grayscale boundary threshold includes at least a grayscale maximum value and a grayscale minimum value; finally, each pixel point in each of the initial sub-images is processed by a linear stretching formula, and the critical threshold corresponding to the binarization process is determined by the Otsu algorithm.

[0082] The linear stretching formula is as follows:

[0083]

[0084] Among them, N1 is the new grayscale value of each pixel, O1 is the original grayscale value, min is the minimum grayscale value, and max is the maximum grayscale value.

[0085] In the above embodiment, step S3 specifically includes:

[0086] S31, perform dilation and erosion operations on each of the sub-images in turn. The morphological operation is to dilate the image first and then erode it. The advantage of this algorithm is that it can eliminate scattered points and burrs in the image. The morphological closing operation is to erode the image first and then dilate it. The advantage of this algorithm is that it can fill holes in the image that are smaller than the selected structural element, so that relatively small discontinuities in the image are connected. For dilation, the purpose of dilation is to expand the boundary points of the material in the image. The dilation operation includes the following calculation formula:

[0087]

[0088] in, S1 ( x , y ) is the selected dilation structure element, and A is the area within the sub-image;

[0089] As can be seen from the above formula, the main steps of the algorithm are to start from the boundary of the original image and the structural elementS1 Smoothing is performed from top to bottom and / or to the right, where if S1 If it intersects with A, keep the pixel, otherwise remove it.

[0090] For corrosion, the purpose of corrosion is to remove the boundary points of the material in the image. The corrosion operation process includes the following calculation formula:

[0091]

[0092] in, S2 ( x , y ) is the selected erosion structure element, and A is the area within the sub-image.

[0093] As can be seen from the above formula, the main steps of the algorithm are to start from the boundary of the original image and the structural element S2 Smoothing is performed from top to bottom and / or from left to right, where if S2 If it is contained by A, then keep the pixel, otherwise remove the pixel. Therefore, it can be seen that after the image is eroded, the smaller connected areas in the image can be removed.

[0094] S32, based on the edge detection algorithm, determine the initial fiber contour points in each of the processed sub-images. When performing edge detection, commonly used edge detection operators include Sobel operator, Prewitt operator, Canny operator, etc., and the contour extraction algorithm is used to process the operator to extract the contour. The extracted contour is composed of a series of points, which are contour points.

[0095] S33, performing feature analysis on the initial fiber contour points, wherein the feature analysis process at least includes analyzing the gradient, curvature, normal and neighborhood features of the initial fiber contour points;

[0096] S34. Use the points determined by the feature analysis as a set of contour point coordinates participating in the neural network training, and perform curve fitting on the contour point coordinate set based on a first power function and / or a second power function to obtain a first contour line segment set in each of the sub-images.

[0097] Preferably, a neural network is used to fit the curve, and the number of times the curve is fitted can be automatically selected according to the characteristics of the initial contour points of the fiber. Generally, a first power or a second power function is selected to fit the curve. In the process of training the neural network, the parameter value is continuously changed along the gradient descent direction to minimize the objective function value to determine the number of coordinate points involved in the training.

[0098] In an embodiment of the present invention, in order to accurately perform three-dimensional display, at least the size data of the material fiber needs to be obtained. The fiber diameter data in the size data is an important indicator for measuring the PBAT degradation membrane material. The acquisition process is described in detail below.

[0099] First, each line segment node in each of the first contour line segment sets is connected to determine the two-dimensional area region within each of the sub-images; then the total number of pixel points in each of the area regions is scanned to obtain the area data and side length data within each of the sub-images; finally, the area data and the side length data are processed to obtain the fiber diameter data within each of the sub-images.

[0100] From the above, it can be seen that the above embodiment indirectly calculates the diameter data based on the area being equal to the product of the side length and the diameter. The side length and area of ​​the rectangle are obtained by scanning the total number of pixel points, and then the fiber diameter data can be obtained, providing a suitable scale for subsequent size screening.

[0101] In the embodiment of the present invention, the influence of three situations needs to be eliminated in order to calculate accurate fiber size data, namely, noise points, fiber intersection points and bonding points. These three nodes can be filtered by building a mapping relationship based on existing technologies (such as big data analysis). Specifically, the first fiber size data is filtered according to the clustering algorithm to obtain the target fiber size data, including:

[0102] According to the pre-constructed noise point mapping relationship, the first fiber size data is subjected to primary screening to obtain corresponding primary result data;

[0103] According to the pre-constructed fiber intersection mapping relationship, the primary result data is subjected to secondary screening to obtain corresponding secondary result data;

[0104] According to the pre-constructed bonding point mapping relationship, the secondary result data is subjected to a tertiary screening to obtain corresponding tertiary result data;

[0105] Based on the second fiber size data, the cluster center of the K-Means clustering algorithm is determined to screen the tertiary result data.

[0106] When performing a three-dimensional display, since three-dimensional and two-dimensional are very different, it is necessary to clarify each direction to obtain accurate three-dimensional display information. The initial electron microscope only obtains electron microscope projection images in different directions. In view of this, the present invention designs the following process to obtain an accurate three-dimensional structure.

[0107] First, the phase shift information of the electron wave function corresponding to each of the group domain image data is determined, which is determined by the different models of the electron microscope and the above principle, and will not be described in detail in the embodiments of the present invention. Then, according to the signal amount scattered at a specific angle and the spatial spectrum of the PBAT degradation film material specimen, the phase shift information is reversely deduced to determine the scattering direction corresponding to each of the group domain image data. Finally, image fitting is performed based on the scattering direction and the target fiber size data, and a reconstructed three-dimensional structure is obtained from the Fourier domain filled with the two-dimensional Fourier transform of the fitting result through a three-dimensional Fourier inverse transform, and the three-dimensional structure is displayed in three dimensions. That is, first, a Fourier transform is performed on the two-dimensional projection to obtain a two-dimensional projection of the molecule in the Fourier space. Based on the Fourier projection slice theorem, after determining the projection angle (scattering direction), a three-dimensional reconstruction can be performed in the Fourier space. Finally, an inverse Fourier transform is performed to obtain the corresponding three-dimensional structure.

[0108] In order to make the three-dimensional display clearer and more intuitive, the embodiment of the present invention also designs a rendering link. Specifically, firstly, the three-dimensional structure is analyzed by big data to obtain the fiber voxel information corresponding to the three-dimensional structure, and the voxel information includes at least color information, texture information, crack information and hole information. Of course, for example, hole information can be displayed in different presentation methods in the fiber, including but not limited to subsequent colors or other line shapes, etc.; based on the WebGL graphics technology standard, the point cloud rendering scale corresponding to the fiber voxel information is determined; and the three-dimensional structure is rendered and displayed at the point cloud rendering scale. Of course, it is preferred to use a three-dimensional rendering engine (such as OSG, MeshLab, PointCloud, etc.) to render the three-dimensional structure, and during the rendering process, ensure that the contour points are highlighted.

[0109] Furthermore, in the above embodiment, after rendering and displaying the three-dimensional structure at the point cloud rendering scale, the method further includes:

[0110] Performing multi-angle and multi-modal transformation simulation processing on the three-dimensional structure, wherein the multi-modal transformation simulation processing at least includes rotation, scaling or translation;

[0111] Based on the result of the simulation processing, the three-dimensional structure is reconstructed to interactively display the three-dimensional structure.

[0112] Another embodiment of the present invention provides a 3D display system for PBAT degradation film materials. Figure 3 , Figure 3The structure block diagram of a 3D display system of a PBAT degradable film material in one embodiment of the present invention is shown. The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the 3D display method of the PBAT degradable film material as described above when executing the computer program.

[0113] The three-dimensional display method and system of PBAT degradation film materials provided by the embodiments of the present invention have the beneficial effects of at least one of the following:

[0114] The two-dimensional electron microscope projection image reflecting the microscopic details of the PBAT degradation membrane material is segmented to reduce the influence of noise and interference in the image, which can highlight the fiber outline in the material and provide accurate data support for subsequent three-dimensional display.

[0115] In order to obtain accurate material fiber information and improve the accuracy of the final three-dimensional display, this solution performs targeted classification processing on the fibers in the material, and incorporates linear segments and at most one nonlinear segment into the sub-image, thereby avoiding the boundary blurring effect caused by the crossing and bonding of too many nonlinear segment fibers in the image; in addition, for calculations such as fiber diameter, the present invention can effectively improve the final fiber diameter accuracy, thereby accurately analyzing the macroscopic properties of the PBAT degradation membrane material. For details, please refer to the table below, which is a preferred result comparison table of the present invention.

[0116]

[0117] (3) By tracking the information of the initial fiber contour points, neural network technology is used to fit the curve. On the one hand, it can approximate a given multivariable continuous function with arbitrary precision, which enables the neural network to handle complex nonlinear relationships and accurately fit various fiber contour curves; on the other hand, the neural network has adaptive and autonomous learning capabilities and can automatically adjust its internal parameters through training to minimize prediction errors. This ability enables the neural network to self-optimize according to different data sets and problems, thereby improving the accuracy and robustness of the fitted fiber contour curve;

[0118] (4) Comprehensive analysis of the original two-dimensional images can improve the display resolution of the three-dimensional structure, making up for the lack of research on the microscopic level of PBAT degradation membrane materials in existing technologies, and helping to more intuitively understand the complex three-dimensional structure, shape and spatial relationship of PBAT degradation membrane materials, thereby promoting the intelligent research process of PBAT degradation membrane materials at a deeper level.

[0119] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A three-dimensional display method of PBAT degradation film material, characterized in that: include: Obtain electron microscope projection images of PBAT degradation membrane material specimens in different directions; Segmenting each of the electron microscope projection images based on the maximum inter-class variance method, and binarizing each of the obtained initial sub-images consisting of at most one nonlinear segment fiber to obtain group domain image data consisting of each of the binarized sub-images; Tracking the initial contour points of the fibers selected in each of the sub-images, and performing curve fitting on the coordinates of the contour points obtained by tracking based on the neural network technology to obtain a first contour line segment set in each of the sub-images; Using an edge algorithm to calculate the first contour line segment set to obtain first fiber size data corresponding to each of the sub-images; By scanning the pixel point information of each set of the first contour line segments, each second fiber size data is obtained, and the first fiber size data is screened according to a clustering algorithm to obtain target fiber size data, wherein the screening process is designed to use the corresponding second fiber size data as a distance metric for clustering; Based on the Fourier projection slice theorem and the target fiber size data, the image data of each group domain after Fourier transformation is reconstructed in three dimensions to obtain the three-dimensional display information corresponding to the PBAT degradation membrane material specimen; The second fiber size data at least includes fiber diameter data; The step of obtaining each second fiber size data by scanning the pixel point information of each first contour line segment set includes: Connecting each line segment node in each of the first contour line segment sets to determine a two-dimensional area region within each of the sub-images; Scan the total number of pixels in each area region to obtain area data and side length data in each sub-image; Processing the area data and the side length data to obtain fiber diameter data in each sub-image; The nonlinear segment fibers include fiber crossing nodes and fiber bonding nodes; Based on the Fourier projection slice theorem and the target fiber size data, the image data of each group domain after Fourier transformation is reconstructed in three dimensions to obtain the three-dimensional display information corresponding to the PBAT degradation membrane material specimen, including: Determining phase shift information of the electron wave function corresponding to each of the groups of domain image data; According to the signal amount scattered at a specific angle and the spatial spectrum of the PBAT degradation film material specimen, the phase shift information is reversely deduced to determine the scattering direction corresponding to each group of domain image data; Image fitting is performed based on the scattering direction and the target fiber size data, a reconstructed three-dimensional structure is obtained from a Fourier domain filled with a two-dimensional Fourier transform of the fitting result by a three-dimensional inverse Fourier transform, and the three-dimensional structure is displayed in three dimensions.

2. The three-dimensional display method of PBAT degradation film material according to claim 1, characterized in that: The segmentation process of each electron microscope projection image based on the maximum inter-class variance method includes: Performing grayscale processing on each of the electron microscope projection images, and calculating the occurrence probability of each grayscale level in the processing result; According to the occurrence probabilities, respectively calculate the pixel probabilities of the fiber crossing nodes in each electron microscope projection image, the pixel probabilities of the fiber bonding nodes in each electron microscope projection image, the pixel probabilities of the linear fiber nodes in each electron microscope projection image, and the pixel probabilities of the background images in each electron microscope projection image; and, Calculate the average gray value of the fiber intersection nodes in each electron microscope projection image, the average gray value of the fiber bonding nodes in each electron microscope projection image, the average gray value of the linear fiber nodes in each electron microscope projection image, and the average gray value of the background image in each electron microscope projection image; Calculate the corresponding inter-class variance according to the pixel probability and the average gray value; The calculation results of the inter-class variance are traversed to determine the segmentation result corresponding to each of the electron microscope projection images.

3. The three-dimensional display method of PBAT degradation film material according to claim 1, characterized in that: Before binarizing each of the obtained initial sub-images consisting of at most one nonlinear segment fiber, the method further comprises: Performing histogram equalization processing on the grayscale histogram of each of the obtained initial sub-images; Based on the equalization processing result, determining the grayscale boundary threshold corresponding to each of the initial sub-images, wherein the grayscale boundary threshold at least includes a grayscale maximum value and a grayscale minimum value; Each pixel point in each of the initial sub-images is processed by a linear stretching formula, and a critical threshold corresponding to the binarization process is determined by the Otsu algorithm.

4. The three-dimensional display method of PBAT degradation film material according to claim 1, characterized in that: The method of tracking the initial contour points of the fibers selected in each of the sub-images and performing curve fitting on the coordinates of the contour points obtained by tracking based on the neural network technology to obtain a first contour line segment set in each of the sub-images includes: Performing dilation and erosion operations on each of the sub-images in turn; Based on an edge detection algorithm, determining initial fiber contour points in each of the processed sub-images; Performing feature analysis on the initial fiber contour points, wherein the feature analysis process at least includes analyzing the gradient, curvature, normal and neighborhood features of the initial fiber contour points; The points determined by the feature analysis are used as a set of contour point coordinates participating in the neural network training, and curve fitting is performed on the contour point coordinate set based on a first power function and / or a second power function to obtain a first contour line segment set in each of the sub-images.

5. The three-dimensional display method of PBAT degradation film material according to claim 1, characterized in that: The step of screening the first fiber size data according to the clustering algorithm to obtain target fiber size data includes: According to the pre-constructed noise point mapping relationship, the first fiber size data is subjected to primary screening to obtain corresponding primary result data; According to the pre-constructed fiber intersection node mapping relationship, the primary result data is subjected to secondary screening to obtain corresponding secondary result data; According to the pre-constructed fiber bonding node mapping relationship, the secondary result data is subjected to a tertiary screening to obtain corresponding tertiary result data; Based on the second fiber size data, the cluster center of the K-Means clustering algorithm is determined to screen the tertiary result data.

6. The three-dimensional display method of PBAT degradation film material according to claim 1, characterized in that: The three-dimensional display of the three-dimensional structure includes: Performing big data analysis on the three-dimensional structure to obtain fiber voxel information corresponding to the three-dimensional structure, wherein the voxel information includes at least color information, texture information, crack information, and hole information; Based on the WebGL graphics technology standard, determining the point cloud rendering scale corresponding to the fiber voxel information; The three-dimensional structure is rendered and displayed at the point cloud rendering scale.

7. A three-dimensional display system of PBAT degradation film material, characterized in that: The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the three-dimensional display method of the PBAT degradation film material according to any one of claims 1 to 6 is implemented.

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